Orient ETL perfomance issue with importing edges to plocal on SSD - orientdb

Question

My goal is importing 25M edges in the graph which has about 50M vertices. Target time:
The current speed of importing is ~150 edges/sec. Speed on remote connection was about 100 edges/sec.
extracted 20,694,336 rows (171 rows/sec) - 20,694,336 rows -> loaded 20,691,830 vertices (171 vertices/sec) Total time: 35989762ms [0
warnings, 4 errors]
extracted 20,694,558 rows (156 rows/sec) - 20,694,558 rows -> loaded 20,692,053 vertices (156 vertices/sec) Total time: 35991185ms [0
warnings, 4 errors]
extracted 20,694,745 rows (147 rows/sec) - 20,694,746 rows -> loaded 20,692,240 vertices (147 vertices/sec) Total time: 35992453ms [0
warnings, 4 errors]
extracted 20,694,973 rows (163 rows/sec) - 20,694,973 rows -> loaded 20,692,467 vertices (162 vertices/sec) Total time: 35993851ms [0
warnings, 4 errors]
extracted 20,695,179 rows (145 rows/sec) - 20,695,179 rows -> loaded 20,692,673 vertices (145 vertices/sec) Total time: 35995262ms [0
warnings, 4 errors]
I tried to enable parallel in etl config, but looks like it is completely broken in Orient 2.2.12 (Inconsistency with multi-threading changes in 2.1?) and gives me nothing but 4 errors in the log above. Dumb parallel mode (running 2+ ETL processes) also impossible for plocal connection.
My config:
{
"config": {
"log": "info",
"parallel": true
},
"source": {
"input": {}
},
"extractor": {
"row": {
"multiLine": false
}
},
"transformers": [
{
"code": {
"language": "Javascript",
"code": "(new com.orientechnologies.orient.core.record.impl.ODocument()).fromJSON(input);"
}
},
{
"merge": {
"joinFieldName": "_ref",
"lookup": "Company._ref"
}
},
{
"vertex": {
"class": "Company",
"skipDuplicates": true
}
},
{
"edge": {
"joinFieldName": "with_id",
"lookup": "Person._ref",
"direction": "in",
"class": "Stakeholder",
"edgeFields": {
"_ref": "${input._ref}",
"value_of_share": "${input.value_of_share}"
},
"skipDuplicates": true,
"unresolvedLinkAction": "ERROR"
}
},
{
"field": {
"fieldNames": [
"with_id",
"with_to",
"_type",
"value_of_share"
],
"operation": "remove"
}
}
],
"loader": {
"orientdb": {
"dbURL": "plocal:/mnt/disks/orientdb/orientdb-2.2.12/databases/df",
"dbUser": "admin",
"dbPassword": "admin",
"dbAutoDropIfExists": false,
"dbAutoCreate": false,
"standardElementConstraints": false,
"tx": false,
"wal": false,
"batchCommit": 1000,
"dbType": "graph",
"classes": [
{
"name": "Company",
"extends": "V"
},
{
"name": "Person",
"extends": "V"
},
{
"name": "Stakeholder",
"extends": "E"
}
]
}
}
}
Data sample:
{"_ref":"1072308006473","with_to":"person","with_id":"010703814320","_type":"is.stakeholder","value_of_share":10000.0} {"_ref":"1075837000095","with_to":"person","with_id":"583600656732","_type":"is.stakeholder","value_of_share":15925.0} {"_ref":"1075837000095","with_to":"person","with_id":"583600851010","_type":"is.stakeholder","value_of_share":33150.0}
Server's specs are: instance on Google Cloud, PD-SSD, 6CPU, 18GB RAM.
Btw, on the same server I managed to get ~3k/sec on importing vertices using remote connection (it is still too slow, but acceptable for my current dataset).
And the question: is it any reliable way to increase speed of importing to let's say 10k inserts per second, or at least 5k? I wouldn't like to turn off indexes, it is still millions of records, not billions.
UPDATE
After few hours the performance continue to deteriorate.
extracted 23,146,912 rows (56 rows/sec) - 23,146,912 rows -> loaded 23,144,406 vertices (56 vertices/sec) Total time: 60886967ms [0
warnings, 4 errors]
extracted 23,146,981 rows (69 rows/sec) - 23,146,981 rows -> loaded 23,144,475 vertices (69 vertices/sec) Total time: 60887967ms [0
warnings, 4 errors]
extracted 23,147,075 rows (39 rows/sec) - 23,147,075 rows -> loaded 23,144,570 vertices (39 vertices/sec) Total time: 60890356ms [0
warnings, 4 errors]